Ontology expansion using entity-association rules and abstract relations

ABSTRACT

A method for expanding an initial ontology via processing of communication data, wherein the initial ontology is a structural representation of language elements comprising a set of entities, a set of terms, a set of term-entity associations, a set of entity-association rules, a set of abstract relations, and a set of relation instances. A method for extracting a set of significant phrases and a set of significant phrase co-occurrences from an input set of documents further includes utilizing the terms to identify relations within the training set of communication data, wherein a relation is a pair of terms that appear in proximity to one another.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 15/007,703, filed on Jan. 27, 2016, and entitled “ONTOLOGYEXPANSION USING ENTITY-ASSOCIATION RULES AND ABSTRACT RELATIONS”, whichclaims priority to U.S. Provisional Patent Application Ser. No.62/108,264, filed Jan. 27, 2015 and U.S. Provisional Patent ApplicationSer. No. 62/108,229, filed Jan. 27, 2015. The disclosures of the abovereferenced applications are all incorporated herein by reference.

BACKGROUND

The present disclosure relates to the field of automated dataprocessing, and more specifically to the application of ontologyprogramming to process and analyze communication data. In the realms ofcomputer and software sciences and information science, an ontology is astructural framework for organizing information regarding knowledge andlinguistics within a domain. The ontology represents knowledge within adomain as a hierarchical set of concepts, and the relationships betweenthose concepts, using a shared vocabulary to denote the types,properties, and interrelationship of those concepts. For example, theontology models the specific meanings of terms as they apply to thatdomain.

SUMMARY

Methods are disclosed herein for expanding an initial ontology viaprocessing of communication data, wherein the initial ontology is astructural representation of language elements comprising a set ofentities, a set of terms, a set of term-entity associations, a set ofentity-association rules, a set of abstract relations, and a set ofrelation instances. An exemplary method includes providing the initialontology, providing a training set of communication data, processing thetraining set of communication data to extract significant phrases andsignificant phrase pairs from within the training set of communicationdata, creating new abstract relations based on the significant phrasepairs, creating new relation instances that correspond to thesignificant term pairs, storing the significant phrases as ontologyterms ontology and associating an entity for the added terms, andstoring the new relation instances and new abstract relations to theinitial ontology.

Also disclosed herein is a method for extracting a set of significantphrases and a set of significant phrase co-occurrences from an input setof documents. An exemplary method includes providing a generic languagemodel and providing the set of documents. The exemplary method extractsa set of significant phrases by, for example, generating asource-specific language model by subdividing each document into meaningunits, accumulating phrase candidates by creating a set of candidateswhere each candidate is an n-gram and integrating over the n-grams tocompute a prominence score for each n-gram and a stickiness core, andfiltering the candidate phrases by calculating a frequency for each ofthe candidate phrases and calculating an overall phrase score for eachof the candidate phrases. The exemplary method can extract significantphrase co-occurrences by, for example, iterating over the meaning unitsand locating the occurrences of individual phrases, counting the numberof co-occurrences of pairs of phrases in the same meaning unit,computing a probability of a phrase and a probability of theco-occurrence of a pair of phrases based on the count, calculating alog-likelihood of the co-occurrence using the probability of the phraseand the probability of the co-occurrence of a pair of phrases andidentifying a significant co-occurrence of the pair of phrases if thelog-likelihood is over a predetermined log-likelihood threshold.

The details of one or more embodiments of the disclosure are set forthin the accompanying drawings and the description below. Other features,objects, and advantages of the disclosure will be apparent from thedescription, drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary embodiment of the ontology process andprogramming disclosed herein.

FIG. 2 depicts examples of hierarchical presentations of ontologiesrefined according to the disclosed process.

FIG. 3 is a schematic diagram of an embodiment of a process for ontologyrefinement.

FIG. 4 is schematic diagram of an embodiment of a process for extractingsignificant phrases and significant phrase co-occurrences.

FIG. 5 is a schematic diagram of an embodiment of a process employing anontology to tag communication data.

FIG. 6 is an exemplary embodiment of an ontology analytics programincorporating and analyzing multiple data platforms.

FIG. 7 is a system diagram of an exemplary embodiment of a system fordeveloping an ontology for processing communication data.

DETAILED DISCLOSURE

According to the present invention, an ontology may be developed andapplied across all types of communication data, for example, all typesof customer interactions (which may include interactions in multiplelanguages) as a tool for processing and interpreting such data. Thecommunication data may document or relate to any type of communication,including communications made via phone, via email, via internet chat,via text messaging, etc. For example, communication data may contain anyspoken content or any written correspondence or communication, includingbut not limited to live speech, audio recording, streaming audio,transcribed textual transcripts, or documents containing writtencommunications, such as manuscripts, web pages, email, physical mail,text messages, chats, etc. In the exemplary context of a customerservice application, the communication data may be between a customerservice agent or an automated system, such as an interactive voiceresponse (IVR) recording, and a customer or caller. While the presentdisclosure is often exemplified herein by describing an embodimentinvolving the analysis of audio data, such as recorded audiotranscripts, it is to be understood that in alternative embodimentsother forms of oral or written communications may be used or analyzed. Aparticular ontology may be defined for and applied to any domain, andother examples include financial services, consumer products,subscription services, or some other business application involvingcommunication data interactions.

In the generation, refinement, or development of an ontology, repeatingpatterns are identified and ranked based upon statistical significancesand then clustered into terms and term relationships. The disclosedsolution uses machine learning-based methods to improve the knowledgeextraction process in a specific domain or business environment. Byformulizing a specific company's internal knowledge and terminology, theontology programming accounts for linguistic meaning to surface relevantand important content for analysis. For example, the disclosed ontologyprogramming adapts to the language used in a specific domain, includinglinguistic patterns and properties, such as word order, relationshipsbetween terms, and syntactical variations. Based on the self-trainingmechanism developed by the inventors, the ontology programmingautomatically trains itself to understand the business environment byprocessing and analyzing a corpus of communication data.

The disclosed ontology programming, once built and refined for aspecific business application, is applied to process communication datato provide valuable analytics for a variety of business needs. Forexample, the ontology programming can then be utilized to detect andsurface meaningful items in a data set, such as a database of recordedemployee-customer interactions, and can mine the data set to extract andanalyze business data based on an enhanced formulization of a company'sinternal knowledge and terminology.

An exemplary embodiment of the presently disclosed ontology solutionincorporates three main stages. As seen in FIG. 1 , the three mainstages include training 1, ontology tagging 2, and ontology analytics 3.The training phase 1 involves internal machine learning in which thesystem learns the customer's specific domain and formulates an initialontology 110. The initial ontology 110 is then passed to the taggingmodule 2. Tagging is a continuous online process that uses the ontologyto tag tracked items in incoming interactions, and stores the taggedinteractions in a persistent repository. Finally, the taggedinteractions are then used by the analytics module 3 to analyze andextract business data based on an enhanced formulization of a company'sinternal knowledge and terminology. A detailed analysis of each stage isaddressed in turn. In certain embodiments, the ontology tagging phase 2and/or the ontology analytics phase 3 can be optional.

Generally, an ontology as

as disclosed herein can be defined as

wherein ε is a set of entities,

is a set of terms,

is a set of term-entity associations,

is a set of entity-association rules, in which,

is a set of abstract relations, and

is a set of relation instances. Terms are individual words or shortphrases that represent the basic units or concepts that might come up inthe communication data. Thus a set of terms

can be defined as a word n-gram that has some meaning. Non-limitingexamples of terms, as used herein, include “device”, “iPhone”, “iPhonefour”, “invoice”, “I”, “she”, “bill”, “cancel”, “upgrade”, “activate”,“broken”, or “cell phone”, “customer care”, or “credit card.” However,these are not intended to be limiting in any manner and are merelyexemplary of basic units or concepts that may be found in a customerservice interaction. In certain embodiments, all words in the corpus, orset of communication data, can only be associated with one term, andeach term can only be counted once.

Development of an ontology involves the identification of termcandidates. A set of communication data used for training purposes isdivided into potential terms, or term candidates. Terms are thenselected from those term candidates. Strong term candidates containwords or word sets that are compact and, in the instance of word sets,the frequency of finding the word set together is very high. An exampleof a term containing a word set is “credit card number,” as those wordsvery often appear together and refer to a particular, defined object. Inaddition, good terms often contain words that make more conceptual sensewhen they are together, as opposed to being on their own. For example,the term “Nova Scotia” is comprised of words that make sense when foundtogether, and would likely not appear or make sense separately.

The frequency that the words of a particular word set, or term, appeartogether may be referred to as the “stickiness” of the term. A “sticky”term is one whose words appear frequently appear together in the corpus.The higher the stickiness ranking, the stronger the term, as it meansthat the term has meaning in the corpus as a concept. Salient terms arethose that stand out or have a higher score relative to similar orneighboring terms. Non-salient terms and less-salient terms are thosethat appear many times or a relatively large number of times in manydifferent contexts. The score of such non-salient or less-salient termsis lowered as compared to the score for salient terms. The logic is thatsalient terms are likely to be meaningful as a concept, whereasnon-salient terms are not likely to express a particular concept. Forexample, the score of the term “account number” would be higher than thescore of the term “the account number” because the word “the” appears inmany different contexts and also by itself. Therefore, the word “the”does not add any significant meaning when joined with the term “accountnumber.”

Entities are broader concepts that encapsulate or classify a set ofterms. Entities describe semantic concepts to which classified terms arerelated. Non-limiting examples of classes, may include “objects”,“actions”, “modifiers”, “documents”, “service”, “customers”, or“locations”. However, these are not intended to be limiting on the typesof entities, particularly the types of entities that may appear in anontology directed to a specific or specialized domain. Thus a set ofentities ε can be organized in a hierarchical tree-like structure, wherefor each entity E∈ε, let π(E) be the parent entity of E.

The set of entities and terms in the ontology are connected by a set ofterm-entity associations

⊆

×

governed by entity-association rules

⊆ε×ε×(ε∪{{tilde over (0)}, {tilde over (1)}, {tilde over (2)}}). Eachterm in the ontology is associated with at least one entity, namely ∀T∃E

T, E

∈

. In some embodiments it is possible for a term to have multiple entityassociations. For example, if the term is “jaguar”, the term may beassociate with the entity “Animal” and with the entity “CarBrand.” Thedistance between a term T and E, denoted d(T, E) is 0 in case

T, E

∈

. Alternatively, if there exists an entity E′ such that

T, E′

∈

and E is an ancestor of E′, then d(T,E)=d(E′,E); in either of these twocases, T<E. Otherwise, d(T,E)=∞.

Abstract relations express relations between two ontology entities. Aset of abstract relations can be defined as

⊆×ε×ε. The distance between two entities E₁, E₂∈ε in the hierarchychain, denoted d (E₁, E₂), can be defined as the number of steps down orup the hierarchy of entities. For example, if E₁ is an ancestor of E₂then d(E₁, E₂) is defined as the number of steps down the hierarchy,whereas if E₂ is an ancestory of E₁ then then d(E₁, E₂) is defined asthe number of steps up the hierarchy of entities. If none of theseconditions apply however, then d(E₁, E₂)=∞.

Relation instances express relations between ontology terms. A set ofrelation instances can be defined as

⊆

×

. For example, the term “pay” may be related to the term “bill” to formthe relation “pay>bill.” In another non-limiting example, the term “pay”may be associated under the entity “action” and the term “bill” may bedefined in the entity “documents”. Still further binary directedrelationships can be defined between these entity/term pairs. Forexample, the action/pay pair may be related to the document/bill pair inthat the payment action requires an underlying document, which may be abill. In another non-limiting example, the term “broken” may be definedin the entity “problems” and the term “iPhone” may be defined in theentity “device”. The problem/broken pair can also have a directedrelationship to the “devices” entity in which the “iPhone” term is aspecific example as represented by the devices/iPhone pair.

FIG. 2 depicts exemplary, non-limiting embodiments of a portion anontology 10, including entities 12 and 14, and terms 16. The arrowsbetween the terms and entities represent some relations that may existin the depicted portion of the exemplary ontology.

FIG. 3 represents an overview of an exemplary training phase 1 forrefining an initial ontology 110. The initial ontology 110 is refined bya step-by-step pipeline process that applies various features to thedefined data. These features include the extracting and surfacing ofwords and phrases in the corpus that helps users make non-trivialobservations about a customer-specific domain.

As exemplified in FIG. 3 , the ontology training process begins with aninitial ontology 110. In certain embodiments, the initial ontology canbe a canned ontology. A canned ontology is an ontology that is developedfor a particular business application or sector—a unique language modelthat reflects, or fits, the relevant business application. For example,a canned ontology may be developed for telecommunications applications,and the telecom canned ontology would differ from that developed forinsurance industry applications, which would differ from the cannedontology developed for the finance industry, etc. A user, or company, ina particular industry may begin the training process by implementing oneor more relevant canned ontologies. The canned ontology is then refinedduring the ontology training phase 1 (FIG. 1 ), to develop a specializedontology for that particular user. If a user starts the training processby implementing more than one canned ontology, the canned ontologies arepreferably unified during the ontology training phase 1, so that one,unified and encompassing ontology structure is developed for the user.

A canned ontology can be developed in various ways. For example, acanned ontology may be developed by taking data samples generated bymultiple different users or classes in a particular industry.Alternatively, a canned ontology may be created by combining multipleontologies developed using sample data sets from a particular industry.For example, multiple users may develop an ontology for their particularbusiness based on their own internal data. Those individual ontologiesmay then be combined through a process of comparison, wherein the commonelements in the ontologies receive heavier weight than the elements thatdiffer. In still other embodiments, a canned ontology could be developedover a series of training processes where one user develops an ontologybased on its data, and then the next user uses the first user's ontologyas a canned ontology input to its training process. Thereby, eachsubsequent user implements a previous user's output ontology as a cannedontology 201 input, and amends or refines that canned ontology throughthe training process to develop its own ontology.

In FIG. 3 , after an initial ontology 110 is obtained, for example acanned ontology, the initial ontology 110 is refined using a developedtraining data set 205. Training data set 205 can be developed by, forexample, accumulating data for each planned ontology that contains arange of communications sufficient to be representative of the languageand linguistic structure of that domain. In certain embodiments, thetraining data set 205 contains various types of data and originates overa sufficiently long time period, for example, between about a monthprevious to the date of implementing the training step up until the mostrecent available data at the time of execution of the training. Thetraining data set 205 may include data from a single platform, such astranscriptions of customer service phone calls, or it may include datafrom multiple platforms, such as customer service calls, emails, webchats, text messages, web page comments, facebook or twitterinteractions, customer surveys, etc (e.g., see FIG. 7 ). In still otherembodiments, the sample data set includes other types of businessdocuments such as, but not limited to, white papers, user manuals,service manuals, or catalogs.

In certain of the embodiments, the ontology training phase 1 is notexecuted until a certain, predefined amount of data is gathered for thetraining. In one embodiment, a configured scheduler may monitor the datagathering process and count the number of records or amount of dataadded. When the number of records or amount of data in the training dataset 205 reaches that predetermined amount, the scheduler may execute theontology training process 1. Alternatively or additionally, thescheduler may monitor the types and/or variety of data added to thetraining data set 205 so as to ensure that the training 301 does notbegin until certain types and/or varieties of data are available to thetraining set. In certain embodiments, the communication data istransformed into a usable format as part of training phase 1. Forexample, audio data from one or more customer interactions between acustomer service agent/IVR and a customer/caller can be automaticallytranscribed into a textual file through speech recognition techniques,and the textual file can be processed as described herein to refine anontology.

Once one or more initial ontologies 110 are selected and a training dataset 205 is developed, the training phase 1 continues by executing atraining module 300, example of which is depicted in FIG. 3 . Ingeneral, training module 300 is an automated process that accepts aninitial ontology and a set of text documents, gives scores to theexisting ontology components based on their relevance to the textdocuments, and enriches the initial ontology with additional terms,abstract relations and relation instances that are characteristic tothose documents.

Starting with the embodiment of FIG. 3 , at step 301, the initialontology 110 and training data set 205 and are is fed into the trainingmodule 300. In certain embodiments, the training module 300 thenextracts a set of significant phrases and significant phrase pairs fromthe training data set 205, by executing, for example, method 400 asshown in FIG. 4 , which is described in further detail below. Inalternative embodiments, the set of significant phrases and significantphrase pairs are entered manually by a user and then used by trainingmodule 300 is the subsequent steps below.

After the set of significant phrases and significant phrase pairs areobtained whether through method 400 or manually, the significant phrasesare then added as ontology terms to the initial ontology 110 andassociated with ontology entities at step 302. As used herein, a phraseϕ=<w₁, . . . w_(n)> comprises a sequence for words. It can be said thata term T′ that comprises the word sequence <w′₁, . . . , w′_(k)> iscontained in ϕ, and denoted by T′⊂ϕ, if k<n and there exists some indexi such that: w_(i)=w′₁, . . . , w_(i+k−1)=w′_(k). A pair of terms T′, T″are mutually contained in ϕ, if both are contained in ϕ (namely T′,T″⊂ϕ) with no overlap between them, namely T′∩T″=ø.

In certain exemplary embodiments, given a set of phrases Φ, trainingmodule 300 performs step 302 by sorting the phrases according to theirlength (shorter phrases are processed first), and then for each ϕ∈Φ, andperforming the following:

-   1. If there exists a term T∈    comprising a word sequence that is identical to ϕ, there is no need    to further process this phrase.-   2. If there exists T₁, T₂∈    that are mutually contained in ϕ, and there exists <E₁, E₂>∈    such that T₁<E₁ and T₂<E₂—then this phrase contains a relation    instance, and there is no need to further process it.-   3. Iterate over all entity-association rules    and compute:

$X^{*} = {\underset{{\langle{E_{1},E_{2},\overset{\sim}{E}}\rangle} \in \mathcal{X}}{argmin}\left\{ {{{{d\left( {T_{1},E_{1}} \right)} + {d\left( {T_{2},E_{2}} \right)}}❘T_{1}},{{{T_{2} \subset \phi} \land {T_{1}\cap T_{2}}} = \varnothing}} \right\}}$

-   -   Namely, identify the most specific rule in        that corresponds to the phrase. If no such rule exists, discard        the phrase ϕ. Otherwise, act according to the third component        {tilde over (E)} in the tuple that comprises the rule X* (recall        that {tilde over (E)}∈ε∪{{tilde over (0)}, {tilde over (1)},        {tilde over (2)}}):        -   If {tilde over (E)}={tilde over (0)}, discard the phrase ϕ.        -   If {tilde over (E)}={tilde over (1)}, add a new term T* into            that corresponds to ϕ, and add a term-entity associations            T*, E₁> into            where E₁ is the entity of the first contained sub-term with            respect to the rule X*, namely T₁.        -   If {tilde over (E)}={tilde over (2)}, add a new term T* into            that corresponds to ϕ, and add a term-entity associations            T*, E₂> into            , where E₂ is the entity of the second contained sub-term            with respect to the rule X*, namely T₂.        -   Otherwise, {tilde over (E)}∈ε, so add a new term T* into            that corresponds to ϕ, and add the term-entity associations            T*,{tilde over (E)}>.

Following step 302, at step 305 new abstract relations are then added tothe initial ontology 110 using the obtained set of significant phrasepairs. As used herein, the set of significant phrase pairs are denotedΨ⊆Φ×Φ, where ω: Ψ→

⁺ is a scoring function that associates a score to a phrase pair.

In certain exemplary embodiments, training module 300 performs step 304in the following way:

-   1. Define a weight function W: ε×ε→    ⁺. Initially, W(E₁, E₂)←0 for each pair of entities <E₁, E₂>.-   2. Iterate over all pairs <ϕ₁, ϕ₂>∈Ψ. If there exists T₁, T₂ ∈    that correspond to ϕ₁, ϕ₂, respectively, iterate over all entity    pairs <E₁, E₂    such that T₁<E₁ and T₂<E₂, and update their weight as follows:

$\left. {W\left( {E_{1},E_{2}} \right)}\leftarrow{{W\left( {E_{1},E_{2}} \right)} + \frac{\omega\left( {\phi_{1},\phi_{2}} \right)}{\sqrt{{d\left( {T_{1},E_{1}} \right)} \cdot {d\left( {T_{2},E_{2}} \right)}}}} \right.$

-   3. Finally, iterate over all entity pairs <E₁, E₂> that are not    already contained in the set of abstract relations    , and do the following:    -   If there exists an abstract relation <E′₁, E′₂        ∈        such that E′₁ is an ancestor of E₁ and E′₂ is an ancestor of E₂        (namely, if <E₁, E₂> refines an existing abstract relation), and        W(E₁, E₂)>W_(R) (where W_(R) is a parameter that serves as a        weight threshold for refining abstract relations), add a new        abstract relation <E₁, E₂> into        .    -   Otherwise, if W(E₁, E₂)>W_(N) (where W_(N)>W_(R) is a parameter        that serves as a weight threshold for completely new abstract        relations), add a new abstract relation <E₁, E₂> into        .

After adding the new abstract relations to the ontology at step 304,training module 300 then adds new relation instances to the ontologybased on the significant phrase pairs Ψ and the scoring function ψ:Ψ→

⁺ at step 306. Training module 300 performs step 304 in for example, thefollowing way: Iterate over all pairs <ϕ₁, ϕ₂>E∈Ψ. If there exists T₁,T₂ ∈

that correspond to ϕ₁, ϕ₂, respectively, compute an entity pair <E₁*,E₂*> such that:

$\left\langle {E_{1}^{*},E_{2}^{*}} \right\rangle = {\underset{{{\langle{E_{1},E_{2}}\rangle} \in \mathcal{A}}|{{T_{1} \prec E_{1}} \land {T_{2} \prec E_{2}}}}{argmin}\left\{ {{d\left( {T_{1},E_{1}} \right)} + {d\left( {T_{2},E_{2}} \right)}} \right\}}$Namely, select the most specific abstract relation <E₁*, E₂*> thatcorresponds to the term pair

T₁, T₂>.

-   -   If no such abstract relation exists, discard the term pair.    -   Otherwise, if ω(ϕ₁,ϕ₂)>τ(τ is a parameter), add the relation        instance <        T₁, E₁*>,        T₂, E₂*>> into        .

Upon completion of the adding of new relation instances at step 306, thetraining phase 1 may be completed 308 and the training module 300 mayoutput and store the refined ontological structure at step 310, which isa refined version of the initial ontology 110 referred to in thediscussion of FIG. 1 . Optionally, the output ontology can be updatedand further refined through a new ontology training 1. For example, atraining phase 1 may be initiated every week, month, year, etc. The newtraining phase may use the existing ontology as a “canned ontology” ofsorts, and may use a recent training data set generated in a definedrecent period. For example, if a new training phase is scheduled tooccur every month to update an existing ontology, then the training dataset would preferably include data collected during the most recentmonth. Thus, the training process 1 will update the existing ontology toincorporate, for example, new terms, term-entity associations, abstractrelations, and relation instances. Thereby, the ontology isautomatically updated to keep up with the changes that occur in thecompany, the industry, the customers, etc.

FIG. 4 depicts a schematic diagram of an exemplary process 400 forextracting a set of significant phrases and a set of significant phraseco-occurrences from an input set of text documents. Given a largecollection of text documents that originate from a common source or arerelated to a certain domain, the exemplary process 400 identifies thephrases in the collection that carry some significance to a specificdomain and identifies pairs of phrases that tend to co-occur in thecollection. Such documents may include articles that are taken from ajournal, social media posts of a specific group, for example. Suchdocuments may even include texts obtained by transcribing audiorecording for a specific company.

Exemplary method 400 beings by accepting as inputs a generic languagemodel L_(G) and a set of documents, wherein generic model L_(G) is amodel that is supposed to model the language distribution of generictexts that are not specific to the common source or its associated fieldof interest.

For ease of description and conception, the exemplary process 400 isdivided into four exemplary phases 402, 404, 406, and 408, three ofwhich can be used for the extraction of significant phrases 402, 404,and 406, and one of which can be used in for the extraction ofsignificant phrase co-occurrences. However, such divisions are notintended to be a limiting of the embodiment or the invention. In certainembodiments not all of the phases need be performed.

In regards to the extraction of a set of significant phrases, anexemplary method 400 can include, for example: first, exemplarylanguage-model generation phase (step 402); second, exemplary candidateaccumulation phase (step 404), and third, exemplary significant phrasefiltering phase (step 406).

As used, L(w₁, . . . , w_(m)) denotes the log-probability of the wordsequence w₁, . . . w_(m), as induced by the language model L. Forexample, if L is a trigram model this log-probability can be expressedas:

${L\left( {w_{1},\ldots,w_{m}} \right)} = {\log_{10}\left( {{p\left( w_{1} \right)} \cdot {p\left( {w_{2}❘w_{1}} \right)} \cdot {\prod\limits_{k = 3}^{n}{p\left( {{w_{k}❘w_{k - 2}},w_{k - 1}} \right)}}} \right)}$

The language-model generation phase step 402 can include, or example,iterating over the input documents and subdividing each document intomeaning units. Meaning units are sequences of words that express anidea. In the context of spoken or informal communications, the meaningunit may be the equivalent of a sentence. Meaning units can dividescripts or utterances into a basic segments of meaning or the equivalentof a sentence, when narrated text is compared to written text. A meaningunit may be a sequence of words spoken by one speaker in a conversationwithout interference. A non-limiting example of a meaning unit in acustomer service context would be the customer statement “I would liketo buy a phone.” In some embodiments, the meaning unit may include somelevel of speaker interference, e.g. very short acknowledgementstatements by the other speaker. All terms in the meaning unit arelinked within the boundaries of the meaning unit. In certain embodimentsthe subdividing above is induced by punctuation marks. However, if theinput texts are generated by transcribing audio data, the subdividingcan be performed using a zoning algorithm; see for example the zoningalgorithm described in U.S. Pat. No. 14,467,783.

Once the meaning units have been subdivided, the Language-modelgeneration phase (step 402) can process each of the meaning units andcount the number of n-grams up to some predetermined order (unigrams,bigrams, trigrams, etc.). An order of 3 or 4 has been seen to yield goodresults. Once the number of n-grams are counted, language-modelprobabilities can be estimated based on the given counters, and asource-specific language model L_(s) can be obtained. One suitable wayof obtaining source-specific language model L_(s), is by a applying asuitable smoothing technique; see, for example: S. F. Chen and J.Goodman, An empirical study of smoothing techniques for languagemodeling, in Computer Speech and Language (1999) volume 13, pages359-394.

In an exemplary candidate accumulation phase (step 404), a set ofcandidates

are created, where each candidate is an n-gram (a sequence of n words),and its respective number of occurrences stored. For example, once thatthe input text documents have been subdivided into meaning units, thefollowing can be performed for each meaning unit:

-   -   For each 1≤n≤n_(max) (n_(max) is the maximal number of words per        phrase):        -   Iterate over all n-grams in the meaning unit. Let <w₁, . . .            , w_(n)            be the words in the current n-gram:            -   1. Compute the prominence score for the n-gram:

${P\left( {w_{1},\ldots,w_{n}} \right)} = {\frac{1}{n} \cdot \left( {{L_{S}\left( {w_{1},\ldots,w_{n}} \right)} - {L_{G}\left( {w_{1},\ldots,w_{n}} \right)}} \right)}$

-   -   -   -   2. If P(w₁, . . . , w_(n))<τ_(P) (where τ_(P) is a                prominence threshold), discard the n-gram.            -   3. Otherwise:                -   If n=1, store the unigram (namely, if the unigram is                    already in                    , increment its occurrences counter—and otherwise                    insert it into                    with a single occurrence).                -   if n>1, compute-the-stickiness score for the n-gram:

${S\left( {w_{1},\ \ldots,w_{n}} \right)} = {{L_{S}\left( {w_{1},\ldots,w_{n}} \right)} - {\max\limits_{1 \leqq k < n}\left\{ {{L_{S}\left( {w_{1},\ldots,w_{k}} \right)} + {L_{S}\left( {w_{k + 1},\ldots,w_{n}} \right)}} \right\}}}$

-   -   -   -   -   If S(w₁, . . . , w_(n))<τ_(S) (where τ_(S) is a                    stickiness threshold), discard the n-gram.                -   Check if there exists in                    a partially overlapping m-gram <u₁, . . . , u_(m)>                    that has a significantly higher significance score                    than <w₁, . . . , w_(n)>, namely (τ_(O) is a                    threshold):                    S(u ₁ , . . . ,u _(m))−S(w ₁ , . . . ,w _(n))>τ_(O)                -   If so, discard the n-gram.                -   Otherwise, store the n-gram in                    .

Once the candidate accumulation phase (step 404) is completed, thesignificant phrase filtering phase (step 406) can calculate a phasescore for each candidate phrase and then keep only those phrases whosescore is above a threshold. The candidate accumulation phase (step 404)can be performed by, for example, integrating over the n-grams in

, let f(w₁, . . . , w_(n)) be the frequency of the phrase <w₁, . . . ,w_(n)>, which can be computed by the counter stored at

normalized by the total number of words encountered in all textdocuments. The overall phrase score for the candidate <w₁, . . . ,w_(n)> can computed in, for example, the following manner:Φ(w ₁ , . . . ,w _(n))α_(P) ·P(w ₁ , . . . ,w _(n))+α_(S) ·S(w ₁ , . . .,w _(n))+α_(f)·log f(w ₁ , . . . ,w _(n))

The significant phrase filtering phase 406 only keeps those phrases forwhich Φ(w₁, . . . , w_(n))>τ_(Φ). Where τ_(Φ) is a threshold for theoverall phrase score α_(P), α_(S) and α_(f) fare scaling parameters thatcan be used to give more significance to one of the measures overanother, these may be optional.

After the set of significant terms are extracted, method 400 continuescan continue to the fourth phase 408 where significant phraseco-occurrences are extracted. Namely, those pairs of phrases that tendto co-occur in the same meaning unit, possible up to some distance. Theextraction of significant phrase co-occurrences can begin by, forexample, iterating over all meaning units and locating the occurrencesof the individual phrases. In certain embodiments longer phrases arepreferred over shorter ones. In case of overlapping occurrences of apair of phrases, only the occurrence of the longer phrases is kept. Asused herein, c(ϕ) denotes the number of occurrences of the phrase ϕ. Thenumber of co-occurrence of pairs of phrases in the same meaning unit iscounted. Depending on parameter, one may count only pairs of phrasesthat are separated by m words at most. As used herein, c(ϕ₁, ϕ₂) denotesthe number of co-occurrences of the phrases ϕ₁ and ϕ₂ Depending onanother parameter, this counter may or may not be sensitive to order.

Based on the counter values, it is possible to compute the probabilityp(ϕ) of a phrase and the probability p(ϕ₁,ϕ₂) of the co-occurrence of apair of phrases. The log-likelihoods of the co-occurrence of phrases ϕ₁and ϕ₂ can be defined as follows:

${\ell\left( {\phi_{1},\phi_{2}} \right)} = {\log\frac{p\left( {\phi_{1},\phi_{2}} \right)}{{p\left( \phi_{1} \right)} \cdot {p\left( \phi_{2} \right)}}}$

After calculating the log-likelihoods, phase 408 then identifies aco-occurrence of a pair of phases <ϕ₁,ϕ₂> if l(ϕ₁,ϕ₂)>τ_(l), where τ_(l)is a log-likelihood threshold.

In summary, the exemplary process 400 described above is capable ofextracting a set of significant phrases and a set of significant phraseco-occurrences from an input set of text documents that related to somespecific domain. Since phrases must be significantly more prominent inthe processed texts with respect to the generic model, the prominencescore can help filter very frequency phrases in the language that carryno special significance in the specific domain. Similarly, thestickiness score can help filter false phrases that are induced by anincidental concatenation of a pair of shorter terms. Process 400 can beused in in ontology refining process described in FIG. 1 to automate theidentification of a set of significant phrases and significant phrasepairs

Referring back to FIG. 1 , once the training phase 1 generates and/orupdates a refined ontology, the ontology can then be used be by anynumber of analytics modules or algorithms (see stages 2 and 3 of FIG. 1) to interpret textual transcripts of customer service interactions forexample. In this context, the interpretation of these customer serviceinteractions can be used to identify content or meaning of a particularcustomer service interaction, or may be used across many customerservice interactions in order to identify topics, trends, or emergingissues.

More specifically, once the initial ontology 110 has been refined intraining stage 1, the system can use a tagging process 2 to tagmeaningful elements in incoming communications data, such as transcribedinteractions, and then store the tagged data for use by a analyticsmodule 3. In certain embodiments for instance, the tagging process 2 caninclude loading key ontology data is loaded into the tagging system, andthen executing the process depicted at FIG. 5 , wherein communicationsdata is fetched 320, tagged 322, and stored 330. Specifically, duringthe loading stage, the ontology and produced during the training stage 1is imported into the tagger's internal memory so that they may be usedfor tagging 2. This data includes ontology data

—i.e., entities, terms, term-entity associations, entity-associationrules, abstract relations, and relation instances. The data is used bythe tagger during the pipeline process of tagging new, incomingcommunications data.

Once the refined ontology data has been loaded into the tagger'sinternal memory, the fetch/tag/store process begins. In the exemplaryembodiment of FIG. 5 , communication data is fetched 320 from the queueand then tagged 322 in accordance with the entity-association rules setup by the ontology data. More specifically, the tagger retrieves acommunication data set, such as the next customer interactiontranscript, from the queue, for example using the Post-ProcessingFramework (PPFW). Depending on the system configurations, the tagger canprocess several interactions simultaneously. The tagger then tags 322meaningful elements in the communication data set, such as abstractrelations and relation instances 328 defined by the ontology.Optionally, the tagging can include tagging of scripts 324 and/ormeaning units 326 (zoning) as well. Optionally, the tagging can includethe tagging of terms as well. The tagging and zoning above can beassisted by, for example, method 400. The tagged communications data isthen stored 330 in a dedicated database, such as a Sybase IQ database.

In one embodiment, for each data set (or bulk of datasets, depending onthe system configuration) the system tags specific elements and thensaves the interaction in a temporary repository, the Context module 321.The process tagging process can be repeated for each element—scripts324, zoning 326 (meaning unit tagging), and relations 328. After theinteractions have been tagged with relations 328, the Write to Databasemodule 333 processes the tagged interaction and sends it to the databasefor storing 330. As described above, the data set can include any numberof types and formats of data. For example, the data set may includeaudio recordings, transcribed audio, email communications, text messagecommunications, etc.

The tagged communications data can then be used to generate any numberof analytics 3 (see FIG. 1 ). For example, the tagged communicationsdata can be processed by a user to develop qualitative and quantitativedata about the user company's customer service interactions. This may bedone, for instance, via analysis of themes. As described above, themesare groups or abstracts that contain synonymous relations, and theyprovide users with a compressed view of the characteristics ofinteractions throughout the data set. As one theme represents a singleconcept occurring in many different calls, themes provide a summary ofthe calls. In this way, themes allow users to quickly and easilyunderstand the nature of a multitude of interactions within the callset.

Any variety of data can be processed and tagged. FIG. 6 illustrates aflow chart demonstrating one embodiment of a communication data set 10that can be tagged 2 and analyzed 3 using an ontology refined accordingto the above-described process. As shown therein, communication data 10may include, for example, audio data, text transcription, email, chat,web page feedback or comments, social networking interactions (such asvia Facebook or Twitter), and customer surveys (e.g. taken via phone oremail). For example, in a customer service application or industry, acustomer service interaction, or series of interactions, may take placeover multiple platforms regarding a certain issue with a particularcustomer. In a preferred embodiment, all such data can be incorporatedand analyzed using the ontology to create a complete picture of theentire customer service system. The communication data 10 is all tagged2 utilizing the ontology. In a preferred embodiment, the ontology isconstructed according to the above described process using a sampledataset that included all types of communication data 10 that will beprocessed using that ontology because such training may provide a morerobust ontology that can account for the linguistic norms of thoseparticular data types. However, the invention also contemplates that arobust ontology may be applied to tag and analyze data types not used inthe training of that ontology. Once a communication data set 10 istagged and analyzed, it is stored and outputted for use and/or review bythe user.

FIG. 7 is a system diagram of an exemplary embodiment of a system 1200for automated language model adaptation implementing an ontologytraining module 300. The system 1200 is generally a computing systemthat includes a processing system 1206, storage system 1204, software1202, communication interface 1208 and a user interface 1210. Theprocessing system 1206 loads and executes software 1202 from the storagesystem 1204, including a software application module 1230. When executedby the computing system 1200, software module 1230 directs theprocessing system 1206 to operate as described in herein in furtherdetail, including execution of the ontology training module 300 andprocess 400.

Although the computing system 1200 as depicted in FIG. 7 includes onesoftware module in the present example, it should be understood that oneor more modules could provide the same operation. Similarly, whiledescription as provided herein refers to a computing system 1200 and aprocessing system 1206, it is to be recognized that implementations ofsuch systems can be performed using one or more processors, which may becommunicatively connected, and such implementations are considered to bewithin the scope of the description.

The processing system 1206 can comprise a microprocessor and othercircuitry that retrieves and executes software 1202 from storage system1204. Processing system 1206 can be implemented within a singleprocessing device but can also be distributed across multiple processingdevices or sub-systems that cooperate in existing program instructions.Examples of processing system 1206 include general purpose centralprocessing units, applications specific processors, and logic devices,as well as any other type of processing device, combinations ofprocessing devices, or variations thereof.

The storage system 1204 can comprise any storage media readable byprocessing system 1206, and capable of storing software 1202. Thestorage system 1204 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 1204 can be implementedas a single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 1204 can further includeadditional elements, such a controller capable, of communicating withthe processing system 1206.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium that can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. It should be understood that in no case is the storage mediamerely a propagated signal.

User interface 1210 can include a mouse, a keyboard, a voice inputdevice, a touch input device for receiving a gesture from a user, amotion input device for detecting non-touch gestures and other motionsby a user, and other comparable input devices and associated processingelements capable of receiving user input from a user. Output devicessuch as a video display or graphical display can display an interfacefurther associated with embodiments of the system and method asdisclosed herein. Speakers, printers, haptic devices and other types ofoutput devices may also be included in the user interface 1210.

As described in further detail herein, the computing system 1200receives communication data 10. The communication data 10 may be, forexample, an audio recording or a conversation, which may exemplarily bebetween two speakers, although the audio recording may be any of avariety of other audio records, including multiple speakers, a singlespeaker, or an automated or recorded auditory message. The audio filemay exemplarily be a .WAV file, but may also be other types of audiofiles, exemplarily in a pulse code modulated (PCM) format and an examplemay include linear pulse code modulated (LPCM) audio data. Furthermore,the audio data is exemplarily mono audio data; however, it is recognizedthat embodiments of the method as disclosed herein may also be used withstereo audio data. In still further embodiments, the communication data10 may be streaming audio or video data received in real time ornear-real time by the computing system 1200.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

What is claimed is:
 1. A method comprising: providing a set of documentsfrom a source by a computing device; iterating over the documents of theset of documents to subdivide the documents of the set of documents intoa plurality of meaning units by the computing device; for each meaningunit, determining an associated count for each n-gram that occurs in themeaning unit by the computing device; for each meaning unit: for eachn-gram that occurs in the meaning unit: computing a prominence score forthe n-gram based on the determined count; if the prominence score forthe n-gram is below a prominence threshold, discarding the n-gram; andif the prominence score for the n-gram is not below the prominencethreshold: if the n-gram is not in a set of candidate n-grams, insertingthe n-gram into the set of candidate n-grams and setting the associatedcount to 1; if the n-gram is in the set of candidate n-grams,incrementing the associated count; and if the n-gram is in the set ofcandidate n-grams and the associated count is greater than 1: computinga stickiness score for the n-gram; and if the stickiness score for then-gram is below a stickiness threshold, discarding the n-gram from theset of candidate n-grams; based on the associated count for each n-gramin the set of candidate n-grams, estimating a language-model probabilityfor each n-gram by the computing device; obtaining a source-specificlanguage model based on the estimated language-model probabilities foreach n-gram by the computing device; loading the source-specificlanguage model into a memory by the computing device; receiving aplurality of transcribed interactions by the computing device; taggingthe plurality of transcribed interactions using the loadedsource-specific language model by the computing device; and generatinganalytics based on the tagged plurality of transcribed interactions bythe computing device.
 2. The method of claim 1, wherein the meaningunits are sentences.
 3. The method of claim 1, wherein the documents aresubdivided into meaning using a zoning algorithm.
 4. The method of claim1, wherein determining the associated count for each n-gram that occursin the meaning unit comprises: determining a count for each unigram thatoccurs in the meaning unit; determining a count for each bigram thatoccurs in the meaning unit; and determining a count for each trigramthat occurs in the meaning unit.
 5. The method of claim 1, furthercomprising: if the stickiness score for the n-gram is not below thestickiness threshold: if there is a partially overlapping n-gram in theset of candidate n-grams that has a greater associated count than then-gram, discarding the n-gram, and if there is no partially overlappingn-gram in the set of candidate n-grams that has the greater associatedcount than the n-gram, incrementing the associated count of the n-gramin the set of candidate n-grams.
 6. The method of claim 5, furthercomprising: for each n-gram in the set of candidate n-grams, calculatinga phrase score for the n-gram; and determining n-grams with a phrasescore above a threshold phrase score as significant phrases.
 7. A systemcomprising: at least one computing device; and a computer-readablemedium storing computer-executable instructions that when executed bythe at least one computing device cause the system to: provide a set ofdocuments from a source; iterate over the documents of the set ofdocuments to subdivide the documents of the set of documents into aplurality of meaning units; for each meaning unit, determine anassociated count for each n-gram that occurs in the meaning unit; foreach meaning unit: for each n-gram that occurs in the meaning unit:compute a prominence score for the n-gram based on the determined count;if the prominence score for the n-gram is below a prominence threshold,discard the n-gram; and if the prominence score for the n-gram is notbelow the prominence threshold: if the n-gram is not in a set ofcandidate n-grams, insert the n-gram into the set of candidate n-gramsand set the associated count to 1; if the n-gram is in the set ofcandidate n-grams, increment the associated count; and if the n-gram isin the set of candidate n-grams and the associated count is greater than1: computing a stickiness score for the n-gram; and if the stickinessscore for the n-gram is below a stickiness threshold, discard the n-gramfrom the set of candidate n-grams; based on the associated count foreach n-gram in the set of candidate n-grams, estimate a language-modelprobability for each n-gram; and obtain a source-specific language modelbased on the estimated language-model probabilities for each n-gram;load the source-specific language model into a memory; receive aplurality of transcribed interactions; tag the plurality of transcribedinteractions using the loaded source-specific language model; andgenerate analytics based on the tagged plurality of transcribedinteractions.
 8. The system of claim 7, wherein the meaning units aresentences.
 9. The system of claim 7, wherein the documents aresubdivided into meaning using a zoning algorithm.
 10. The system ofclaim 7, wherein determining the associated count for each n-gram thatoccurs in the meaning unit comprises: determining a count for eachunigram that occurs in the meaning unit; determining a count for eachbigram that occurs in the meaning unit; and determining a count for eachtrigram that occurs in the meaning unit.
 11. The system of claim 7,further comprising computer-executable instructions that when executedby the at least one computing device cause the system to: if thestickiness score for the n-gram is not below the stickiness threshold:if there is a partially overlapping n-gram in the set of candidaten-grams that has a greater associated count than the n-gram, discard then-gram, and if there is no partially overlapping n-gram in the set ofcandidate n-grams that has the greater associated count than the n-gram,incrementing the associated count of the n-gram in the set of candidaten-grams.
 12. The system of claim 11, further comprisingcomputer-executable instructions that when executed by the at least onecomputing device cause the system to: for each n-gram in the set ofcandidate n-grams, calculate a phrase score for the n-gram; determinen-grams with a phrase score above a threshold phrase score assignificant phrases.
 13. A non-transitory computer-readable mediumstoring computer-executable instructions that when executed by at leastone computing device cause a system to: provide a generic languagemodel; provide a set of documents from a source; iterate over thedocuments of the set of documents to subdivide the documents of the setof documents into a plurality of meaning units; for each meaning unit,determine an associated count for each n-gram that occurs in the meaningunit; for each meaning unit: for each n-gram that occurs in the meaningunit: compute a prominence score for the n-gram based on the determinedcount; if the prominence score for the n-gram is below a prominencethreshold, discard the n-gram; and if the prominence score for then-gram is not below the prominence threshold: if the n-gram is not in aset of candidate n-grams, insert the n-gram into the set of candidaten-grams and set the associated count to 1; if the n-gram is in the setof candidate n-grams, increment the associated count; and if the n-gramis in the set of candidate n-grams and the associated count is greaterthan 1: computing a stickiness score for the n-gram; and if thestickiness score for the n-gram is below a stickiness threshold, discardthe n-gram from the set of candidate n-grams; based on the associatedcount for each n-gram in the set of candidate n-grams, estimate alanguage-model probability for each n-gram; and obtain a source-specificlanguage model based on the estimated language-model probabilities foreach n-gram; load the source-specific language model into a memory;receive a plurality of transcribed interactions; tag the plurality oftranscribed interactions using the loaded source-specific languagemodel; and generate analytics based on the tagged plurality oftranscribed interactions.
 14. The computer-readable medium of claim 13,wherein the meaning units are sentences.
 15. The computer-readablemedium of claim 13, wherein the documents are subdivided into meaningusing a zoning algorithm.
 16. The computer-readable medium of claim 13,wherein determining the associated count for each n-gram that occurs inthe meaning unit comprises: determining a count for each unigram thatoccurs in the meaning unit; determining a count for each bigram thatoccurs in the meaning unit; and determining a count for each trigramthat occurs in the meaning unit.
 17. The computer-readable medium ofclaim 13, further comprising computer-executable instructions that whenexecuted by the at least one computing device cause the system to: ifthe stickiness score for the n-gram is not below the stickinessthreshold: if there is a partially overlapping n-gram in the set ofcandidate n-grams that has a greater count than the n-gram, discard then-gram, and if there is no partially overlapping n-gram in the set ofcandidate n-grams that has the greater count than the n-gram, incrementthe count associated with the n-gram in the set of candidate n-grams.18. The computer-readable medium of claim 17, further comprising: foreach n-gram in the set of candidate n-grams, calculate a phrase scorefor the n-gram; determine n-grams with a phrase score above a thresholdphrase score as significant phrases.